1. Field of the Invention
The present invention relates in general to semiconductor wafer manufacturing. More particularly, the present invention relates to analyzing visual defects of a semiconductor wafer by utilizing an asymmetric review methodology in the manufacturing process of the semiconductor wafer.
2. Description of Related Art
Manufacturing processes for submicron integrated circuits (ICs) require strict process control for minimizing defects on integrated circuits. Defects are the major “killers” of devices formed during manufacturing, resulting in yield loss. Along with the trend of miniaturization of devices, the tolerance of defect size in IC chips becomes more stringent. Visual defect analysis has become an essential part of IC manufacturing processes. In-line inspection and review are important for determining whether wafers have become contaminated with particles due to a tool malfunction or process problem.
It is believed that the visual defects with larger defect size have greater “killing power” and will cause severe damage on the semiconductor circuit and deteriorate the performance of the semiconductor device. The design rules of devices have been rapidly shrinking. Accordingly, when inspecting semiconductor wafers in the manufacturing process, the sensitivity of the inspection must increase for capturing defects with defect sizes down to or even smaller than the design rule scale, which defects might cause a short or prevent the normal operation of the semiconductor device. Due to the greater inspection sensitivity, the reported defect counts are likely to increase. In addition, since small size defects are much more common than those of larger size, small size defects will likely greatly dominate the distribution. FIG. 1 is a histogram of defects that might be found on a typical wafer, broken down into 0.05 micron size ranges. As can be seen, the number of defects in the smallest size range is vastly larger than the number of defects in any other size range.
FIG. 2 is a flowchart illustrating a conventional process for analyzing defects on a semiconductor wafer. In step 202, an automatic machine vision tool is used to inspect some or all dice on the wafer for defects. The tool reports a location and size for each defect (referred to at this point as a “preliminary” defect) found. The preliminary defect information is transmitted to a database management system (DBMS), which sorts the preliminary defects into size ranges which typically are user-selectable to a limited extent, and reports a count of the number of preliminary defects found in each size range.
Most defects found visually, however, are not yield-killing. That is, they will not affect the operation of the device. But no good automated method has been found yet for reliably determining which preliminary defects are yield killers and which are not. Therefore most defect analysis processes include a subsequent “review” step for human review of the preliminary defects via such equipment as an optical microscope (OM) or a scanning electron microscope (SEM). The review step is expensive, however, both because the review station equipment is expensive and because the time required for human review of a defect is substantial. It is common practice, therefore, to select for review only a small percentage of the preliminary defects on a wafer. The selection can be made in the DBMS normally, by choosing one or more size ranges for review, or it can be made randomly by the review station, by setting a review ration (percentage) in an operation menu. Alternatively, an operator can manually select defects for review from an operation screen. Combinations of these selections methods also can be used.
Sophisticated statistical sampling methods may be used to improve the representativeness of this random selection. In step 204, therefore, a small percentage, specified by the operator, of the preliminary defects is selected for review. In step 206, the selected preliminary defects are reviewed in the review station and the results are statistically extrapolated to predict the yield-killing effect on each die or the entire wafer.
In the past, the database systems used to select defects randomly did not generally use defect size in determining its random sampling of defects for further analysis. The operator could specify that some percentage (e.g. 50%) of the defects are to be selected, and could even specify from which size ranges the defects should be selected. But the operator could not otherwise specify that the selection was to emphasize defects having a larger size. The random selection made by the DBMS system would give each defect in the specified size ranges an equal probability of selection regardless of its size. This sampling algorithm was therefore doubly-deficient: not only did it fail to emphasize the types of defects that were more likely to be yield killers, but because the number of defects of each size typically decreases rapidly as the size increases, a random sampling in which each defect is given equal likelihood of selection regardless of its size would in fact greatly emphasize smaller defects, which are less likely to be yield killers. Much time and expense has therefore in the past been used unproductively by obtaining redundant information from reviewing numerous small defects, while missing potentially important information that could be obtained by reviewing more larger-size defects.